Woodhouse Steven, Moignard Victoria, Göttgens Berthold, Fisher Jasmin
Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.
Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
Immunol Cell Biol. 2016 Mar;94(3):256-65. doi: 10.1038/icb.2015.102. Epub 2015 Nov 18.
New single-cell technologies readily permit gene expression profiling of thousands of cells at single-cell resolution. In this review, we will discuss methods for visualisation and interpretation of single-cell gene expression data, and the computational analysis needed to go from raw data to predictive executable models of gene regulatory network function. We will focus primarily on single-cell real-time quantitative PCR and RNA-sequencing data, but much of what we cover will also be relevant to other platforms, such as the mass cytometry technology for high-dimensional single-cell proteomics.
新的单细胞技术能够轻易地在单细胞分辨率水平上对数千个细胞进行基因表达谱分析。在本综述中，我们将讨论单细胞基因表达数据的可视化和解读方法，以及从原始数据到基因调控网络功能的预测性可执行模型所需的计算分析。我们将主要聚焦于单细胞实时定量PCR和RNA测序数据，但我们所涵盖的大部分内容也将与其他平台相关，例如用于高维单细胞蛋白质组学的质谱流式细胞技术。
